Sensor data exists. Intelligence does not.
The systems that power industry, infrastructure, and cities still operate without a unified representation of reality. Sensor data exists, but it is fragmented, unstructured, and not directly usable by AI.
We are building the foundation model for the physical world — one that turns raw sensor data into structured, queryable 3D intelligence. Not just reconstruction. Not just visualization. A system that encodes geometry, materials, and spatial relationships into a form AI can understand and reason over.
That is what we are building.
Origin · We started in the field
Rubble still settling. Rescue teams waiting on spatial intelligence that did not yet exist.
We did not start in a lab. We started in the field.
2023 Turkey earthquake. Buildings collapsing. We deployed computer vision on satellite and drone imagery for real-time rescue coordination while the rubble was still settling.
That is where we learned what spatial intelligence actually requires: speed measured in seconds not hours, robustness with imperfect data, and answers that people stake lives on.
We carried that standard into everything we built after. What we built for the hardest conditions turned out to be the foundation for every physical industry.
We reconstruct.
We understand.
We simulate.
| 01 | Reconstruct | [metric-3d][any-sensor] | Real environments from real sensors. Any video, any device, any modality. Metrically accurate 3D in minutes, not hours. Phone cameras to LiDAR to satellite imagery. |
| 02 | Understand | [queryable][scene-graph] | Every object, every material, every defect, every spatial relationship — not as pixels on a screen, but as a structured, queryable graph that an engineer or an AI agent can reason over. |
| 03 | Simulate | [real-physics][real-terrain] | Fire spread on actual terrain with detected fuel types. Structural failure on actual geometry with detected material properties. Flood inundation on actual topography. Corrosion progression at actual measured rates. |
Physical Economy · Infrastructure at scale
Today they build this from scratch, costing millions in R&D. Tomorrow they call our API.
Physics doesn't change. Only how well the model understands it.
The more environments we reconstruct, the stronger our priors. The more defects we classify, the more precise our detection. The more physics we simulate, the more accurate our understanding becomes.
Every scan, every asset, every vertical is another sample of the same underlying reality. Our models don't just accumulate data. They converge on the physical rules that govern structures, materials, and failure.
This creates a flywheel with no ceiling. Over time, we build a persistent memory of the physical world that new entrants cannot replicate.
Text existed before GPT. The internet existed before Google.
Three million drone sites. LiDAR on every phone. A $37B inspection market. Zero intelligence layers on any of it — before Percept AI.
The capture infrastructure is built. Sensors are everywhere, cheap, ubiquitous. The intelligence layer that turns all of that data into understanding — and that understanding into prediction — does not exist yet.
We are building it.